Joint frequency domain and reconstructed phase space features for speech recognition

A novel method for speech recognition is presented, utilizing nonlinear/chaotic signal processing techniques to extract time-domain based, reconstructed phase space features. This work examines the incorporation of trajectory information into this model as well as the combination of both MFCC and RPS feature sets into one joint feature vector. The results demonstrate that integration of trajectory information increases the recognition accuracy of the typical RPS feature set, and when MFCC and RPS feature sets are combined, improvement is made over the baseline. This result suggests that the features extracted using these nonlinear techniques contain different discriminatory information than the features extracted from linear approaches alone.